Python pacakge to flatten and fold parameter data structures.

pip install paragami==0.42


"Parameter origami": paragami.

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Parameter folding and flattening, parameter origami: paragami*!

This is a library (very much still in development) intended to make sensitivity analysis easier for optimization problems. The core functionality consists of tools for "folding" and "flattening" collections of parameters -- i.e., for converting data structures of constrained parameters to and from vectors of unconstrained parameters.

For background and motivation, see the following papers:

Covariances, Robustness, and Variational Bayes Ryan Giordano, Tamara Broderick, Michael I. Jordan

A Swiss Army Infinitesimal Jackknife Ryan Giordano, Will Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick

Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics Runjing Liu, Ryan Giordano, Michael I. Jordan, Tamara Broderick

Using the package.

We welcome new users! However, please be aware that the package is still in development. We encourage users to contact the author (github user rgiordan) for advice, bugs, or if you're using the package for something important.


To install the latest tagged version, install with pip:

python3 -m pip install paragami.

The paragami package is under rapid development, so you may want to clone the respository and use the master branch instead.

Note: In order to use the functions in sparse_preconditioners_lib, you must additionally manually install scikit-sparse, which requires the C++ libraries in libsuitesparse-dev. Most users will not require this functionality so scikit-sparse is not installed by default with paragami for simplicity. See the scikit-sparse requirements for more details on installation.

Documentation and Examples.

For examples and API documentation, see readthedocs.

Alternatively, check out the repo and run make html in docs/.

* Thanks to Stéfan van der Walt for the suggesting the package name.